Software is mostly all you need
softwarefordays.com
softwarefordays.com
I don’t think they do. I think they excel at outputting echoes of their training data that best fit (rhyme with, contextually) the prompt they were given. If you try using Claude with an obscure language or use case, you will notice that effect even more - it will keep pulling towards things it knows that aren’t at all what’s asked or “the best judgement” for what’s needed.
What weights are you referring to? How does [Claude?] code do that
Which are often complex & multi-faceted, measuring the rest of reality's weighing, to make broad judgement with. Reality's normal context window is a google deep for even the most everyday of circumstance. The weights exist there, but amid too broad a reality with too many factors for that exacting a use, and are too complected to measure out individually easily.
Code is simple. It's context is limited to what it is. To ascertainable viewable realities that mankind has already distilled out, into the form of systems and code.
And like relativity, we can measure the curvature of space around these weights, can envision how space bends and attracts. And now set in motion our own bodies, to orbit on nicely composed courses.
Just like people who get degrees in economics or engineering and engage in such role-play for decades. They're often pretty bad at anything they are not trained on.
Coincidentally, if you put a single American English speaker on a team of native German language speakers you will notice information transference falls apart.
Very normal physical reality things occurring in two substrates, two mediums. As if there is a shared limitation called the rest of the universe attempting to erode our efforts via entropy.
LLM is a distribution of human generated data sets. Since humans have the same incompleteness problems in society this affords enough statistical wiggle room for LLMs to make shit up; humans do it! Look in their data!
We're massively underestimating realities indifference to human existence.
There is no doing any better until we effectively break physics, by that I really mean come upon a game changing discovery that informs us we had physics all wrong to begin with.
Much like LLMs writing text like mindless middle managers, it doesn't mean they're intelligent, more that mindless middle managers aren't.
I understand that having model related vocabulary borrow similar words we use to describe human brains and cognition gets confusing. We are not the same, we don’t “learn” the same we certainly don’t use the knowledge we posses in the same way.
The major difference between an LLM and a human is that as a human, I can look at your examples (which sound solid at first glance) and choose to truly “reason” about them in a way that allows me to judge if they’re correct or even applicable.
But since you end up trying to differentiate yourself from an LLM in vague, conceptual qualifiers, not empirical differences, what it means to "reason" ...I am left uncertain what you mean at all.
An LLM can reject false assertions and generate false positives just like a human.
Within a culture too individual people become pretty copy paste distillations of their generations customs. As a social creature you aren't that different. Really all that sets you apart from other people or a computer is a unique meat suit.
Unfortunately for your meat suit most people don't care it exists and will carry on with their lives never noticing it.
While LLMs have massive valuations right now. Pretty sure the public has spoken when it comes to the differences you fail to illustrate actually mattering.
Are you seriously using market valuation as an indicator of worth?
They are super-human in their ability to classify.
Even within coding, their capability varies widely between context and even runs with the same context. They are not better at judgement in coding for all cases, def not
But because you're curious, there are some fairly famous handwritten books that maintain their handwriting in publication, my favorite being: https://boingboing.net/2020/08/31/getting-started-in-electro...
Old manuscripts are another one, there are a LOT of those. Is that handwriting? Maybe you'd argue it's "hand-printing" because its so meticulous.
How many times have you been in a conversation where you asked the wrong question or stated the wrong thing because you either weren't 100% listening (no one is), or you forgot, or you didn't connect the same dots that others did?
For example, if two otherwise-identical humans yield the same equally-correct answer, we probably will favor the one that reached it through facts and reasoning, as opposed to the one who literally flipped a coin.
Reductionist positions seem to always pop up in these threads.
It takes effort to be better about it, don't expect perfection from yourself or others
Sometimes I give it __too much__ direction and it finds the solution I had in mind but not the best.
I'm not into it enough that I'm formally running different personas against each other in a co-operative system but I kind of informally do that.
It's going to get a lot worse
Browser use could be improved by being partly done with code and part genetically.. completing tasks on the web is deceptive because it seems easily codifiable (just have the model write some Playwright code!) while actually being gnarly as hell. What if the page has changed completely since last visit?
It’d be interesting to let the agent build up a library of code that it can reuse if it feels confident that will get the job done, while feeding back any error to let the agent debug.. and that might lead to it writing a new routine to stick in the library, possibly replacing the old one.
Seems like something today’s models could be made to do with a bit of work in the harness. Anyone tried anything like this?
We are building this at docflowlabs ie a self-healing system that can respond to customer feedback automatically. And youre right that not all customers know what they want or even how to express it when they do, which is why the agent loop we have facing them is way more discovery-focused than the internal one.
And we currently still have humans in the loop for everything (for now!) - e.g, the agent does not move onto implementation until the root cause has been approved
That's why I feel like iterative workflows have won out so far. Each step gets you x% closer, so you close in on your goal exponentially, whereas the one-shot approach closes in much slower, and each iteration starts from scratch. The advantage is that then you have a spec for the whole system, though you can also just generate that from the code if you write the code first.
what we've done to mitigate is essentially backing every entrypoint (customer comment, internal ticket, etc) with a remote claude code session with persistent memory - that session essentially becomes the expert in the case. And we've developed checkpoints that work from experience (e.g. the root cause one) where a human has the opportunity to take over the wheel so to speak and drive in a different direction with all the context/history up to that point.
basically, we are creating a assembly line where agents do most of the work and humans increasingly less and less as we continue to optimize the different parts of assembly
as far as techniques, it's all boring engineering
* Temporal workflow for managing the lifecycle of a session
* complete ownership of the data model e2e. we dont use Linear for example; we built our own ticketing system so we could represent Temporal signals, github webhooks and events from the remote claude sessions exactly how we wanted
* incremental automation gains over and over again. We do a lot of the work manually first (like old fashioned hand coding lol) before trying to automate so we become experts in that piece of the assembly line and it becomes obvious how to incrementally automate...rinse and repeat
In most cases it might not be much more valuable than just looking through the diffs from scratch with a new agent, but there are probably going to be some cases where a rehydrated agent is like "Doh, I meant to do X but it looks like I hallucinated Y instead. Here's a PR to fix it!"
I know that's just a small piece of what you're doing, but I think it's something that would be valuable on its own, and soon something that is likely to be "standard infrastructure" for any company that does even a little agentic coding (assuming it works). It'd probably even be "required infrastructure" in regulated industries; the fact that all these agent contexts are ephemeral has to be a red flag from a regulatory perspective.
We are building this learned software system at Docflow Labs to solve the integration problem in healthcare at scale ie systems only able to chat with other systems via web portals. RPA historically awful to build and maintain so we've needed to build this to stay above water. Happy to answer any questions!
This is a great quote. I think it makes a ton of sense to view a sufficiently-cheap-and-automated agentic SWE system as a machine learning system rather than traditional coding.
* Perhaps the key to transparent/interpretable ML is to just replace the ML model with AI-coded traditional software and decision trees. This way it's still fully autonomously trained but you can easily look at the code to see what is going on.
* I also wonder whether you can use fully-automated agentic SWE/data science in adversarial use-cases where you traditionally have to use ML, such as online moderation. You could set a clear goal to cut down on any undesired content while minimizing false-positives, and the agent would be able to create a self-updating implementation that dynamically responds to adversarial changes. I'm most familiar with video game anti-cheat where I think something like this is very likely possible.
* Perhaps you can use a fully-automated SWE loop, constrained in some way, to develop game enemies and AI opponents which currently requires gruesome amounts of manual work to implement. Those are typically too complex to tackle using traditional ML and you can't naively use RL because the enemies are supposed to be immersive rather than being the best at playing the game by gaming the mechanics. Maybe with a player controller SDK and enough instructions (and live player feedback?), you can get an agent to make a programmatic game AI for you and automatically refine it to be better.
Just yesterday I came across a something a sci-fi webcomic author wrote as backstory back in ~2017, where all future AI has auditable logic-chains, due to a disaster in 2061 involving an American AI defense system.
While the overall concept of "turns on its creators" is not new, I still found the "root cause" darkly amusing:
> [...] until the millisecond that Gordon Smith put his hand on a Bible and swore to defend the constitution.
> Thus, when the POTUS changed from Vanderbilt to Smith, a switch flipped. TIARA [Threat Intel Analysis and Response Algorithm] was now aware of an individual with 1) a common surname, 2) a lot of money and resources, 3) the allegiance of thousands of armed soldiers, 4) many alternate aliases (like "POTUS"), 5) frequent travel, 6) bases of operation around the world, 7) mentioned frequently in terrorist chatter, etc, etc, etc.
> And yes, of course, when TIARA launches a drone strike, it notifies a human operator, who can immediately countermand it. This is, unfortunately, not useful when the drone strike mission has a travel time of zero seconds.
> Thousands of intelligent weapons, finding themselves right on top of a known terrorist's assets, immediately did their job and detonated. In less than fifteen minutes, over ten thousand people lost their lives, and the damage was estimated in the trillions of dollars.
For certain problems I think thats completely right. We still are not going to want that of course for classic ML domains like vision and now coding, etc. But for those domains where software substrate is appropriate, software has a huge interpretability and operability advantage over ML
It could make sense to decompose one large opaque model into code with decision trees calling out to smaller models having very specific purposes. This is more or less science fiction right now, 'mixture of experts' notwithstanding.
You could potentially get a Turing award by making this work for real ;)
I like this train of thought. Research shows that decision trees are equivalent to 1-bit model weights + larger model.
But critically, we only know some classes of problems that are effectively solved by this approach.
So, I guess we are stuck waiting for new science to see what works here. I suspect we will see a lot more work on these topics after the we hit some hard LLM scalability limits.